Our network architecture is illustrated in Fig. REF . It is composed of a generator \(G\)  and a discriminator \(D\) . \(G\)  can be any FCN-based segmentation network [1]}, [2]}, [3]}, which is further divided into a feature extractor \(F\)  and a classifier \(C\) . We attach the SIB on the output from last convolutional layer of \(F\) . \(D\)  is a CNN-based binary classifier with a fully-convolutional output [4]}, which attempts to distinguish whether a latent feature is from source or target domain.
